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Fig. 2 Flow chart of Jaya algorithm (Source Int. J. Ind. Eng, p. 21)
A (i, j, b), A (i, j, w) are input variable at best, worst functional value. r (i, j,
1), r(i, j, 2) are random numbers. Values of input variables are updated, assessed
with previous results. Superior results are treated for immediate iteration. Process is
continued till termination criteria reached. Flow chart of Jaya is shown in Fig. 2.
3.1 Applications of JAYA Algorithm
Ravipudi et al. proposed and applied MO-SAMP for MOO of design parameters of
thermal devices namely two-stage thermoelectric cooler, two-stage irreversible heat
pump and plate-fin heat exchanger (PFHE), transcritical CO 2 cycle and irreversible
Carnot power cycle. In all thermal devices, superior results are obtained using MOSAMP algorithm than GA, PSO, TLBO, MOTLBO [23]. Rao et al. implemented
Mo-Jaya to machining processes—plasma arc machining (PAM), electro-discharge
machining (EDM) and micro-EDM. In PAM optimization, MO-Jaya produces 50
solutions in 8 iterations, EDM process, 50 trade of solutions produced in 20 iterations by MO-Jaya. In micro-EDM process, MO-Jaya has taken 11 iterations to
produce 50 trade of solutions [24]. Rao and Ankit implemented Elite Jaya algorithm
to different heat exchangers design like STHE and PFHE, both cases Elite Jaya results
are comparatively improved than TLBO and GA [25].
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